Deep Learning and Neuroscience

Deep Leaning the way to advance and improve

Learn how advanced technological methods and applications can be combined with Neuroscience

About the article

In the article, we will see what is Deep Learning, how it can help in neuroscience, what is the relation between them and how advanced technology can be adjusted to our needs.

Deep Learning was first introduced in 1986 and since then it has made tremendous progress through the years by giving us the opportunity to use it in our daily life. Such us voice, image and video recognition, which some of these technologies are used by our mobile phones.

Neuroscience is the scientific study for the neurons and the nervous system. This study combines physiology, anatomy, mathematical modelling, psychology and much more to give us an understanding of how of the biological basis of learning, memory, behaviour, perception and consciousness are working.

And lastly, we will see how the two of them can be combined and help in further research to find answers about the complex procedures of the brain.

Deep Learning

Deep Learning is a great innovation that allows us to automate and train different things to improve our life. For example, we can use that for autonomous driving, teaching robots how to do housework jobs like cleaning.

The scale always depends on our needs, either it is for consumer or industrial use. Big companies over the last years have changed the way of thinking and they adjusting their plans with Artificial Intelligent applications.

Here are some of the sectors of what Artificial Intelligence can achieve. Healthcare, Financial Services, Creative Arts, Marketing, Manufacturing, Social Media. Deep Learning has various use cases and the main attribute is the processing of big data and being able to give simple answers and predictions.

Neuroscience

Neuroscience has allowed neuroscientists to study the nervous system in all its aspects: how it is structured, how it works, how it develops, how it malfunctions, and how it can be changed.

It is a very complex and time-consuming process that requires patience, experimentation and continuously researching new data. It is the main part that handles all the operations of the human.

With a first glance, this might seem unrelated to how technology and especially Deep Learning can help with that. One of the main reasons for this advancement in the research is how we managed to gather all this information, process it and represent it in a simplified version. And this was accomplished with the help of engineers to develop specialized machines and applications to analyze this information.

What is the point of view for Deep Learning in the future

With the current results from Deep Learning and Artificial Intelligence that have been used, we can only imagine how things will improve and become even better. Ideas and dreams of the past are gone because those ideas now are here and each day they evolving.

I strongly believe that Deep Learning will be the main aspect that will drive the technology even further. What we have accomplished today is only the tip of the iceberg. New methods and techniques have been created and existent models every day are being improved.

Deep Learning has given us plenty of possibilities for situations that we couldn’t do anything better. I already see how this has helped us to automate various things and I cannot wait to see in the future their progress into a system that will co-exist with our work and make it easier, more accessible and giving us the insight to decide on critical situations what is the best way to follow.

What are the benefits of Deep Learning and what is it good for

The benefits of Artificial Intelligence are a lot and here are some examples.

It is already used in the new electric cars that have an autonomous driving system. Drones as well are using this system to help for surveillance purposes, programs are created based on that to help scientists to search for a cure to certain diseases, big companies are feeding data on a daily basis to help them take a marketing decision. AI assistants such as Cortana, Siri, Alexa and Holly are being used from mobile phone companies that we are using to book an appointment, create notes, search for something on the internet and receive an answer without moving our hands only with voice commands.

From my point of view, Deep Learning has a lot of benefits and mostly is to automate, process huge amount of information, precision, consistency and improvement in manufacturing procedures, diagnosis analysis and most important the ability to create something once and let it learn from its mistakes.

What are the drawbacks of Deep Learning

Certainly, every great idea or innovation has some drawbacks, some of them may be critical or not.

For that, I would like to start from the least important. Developing an AI program is a complex procedure, time-consuming, lots of research and analysis, it requires time for training and finally, it needs time for optimization, tuning and further time for training.

One major problem that I would like to discuss is the decision between a large neuron network and having a lot of training examples. Here we have a dilemma because having a huge network that means it will need more time to train before it is able to provide results and also having a lot of training examples is not easy to find and secondly we don’t want to overfit the algorithm. That means we have to find the balance between those two and this also implies that in each situation a different approach has to be made.

When we create an algorithm that doesn’t mean it can be used for every case. Even similar situation may need a different model that will have different parameters and so on we have to follow the same procedure again.

Lastly, I would like to mention the most important factor for Deep Learning, which is security. We all know or at least have heard about vulnerabilities and data security. This is really important for those systems because if someone has access in the process of the training of the algorithm by any mean that would be an issue. When we are dealing with a new technology we cannot know what issues may appear in the future. However, what we can do is to ensure that the methods we use are safe and wait until something new comes out as an issue and resolve it.

What is the value to use Deep Learning

The value that we are getting from Deep Learning has different aspects. First of all, it will offer new job positions within the industry for people who are working in machine learning and Artificial Intelligence.

Will enhance the research speed and efficiency by helping in the analysis of big data and complex calculations. These models are designed to handle a lot of parameters and mathematic models.

It will be able to help many people in simple needs, for example requesting information from some government services which can take days to reply and automate this process.

The main value of Deep Learning is that it can be adjusted to almost any situation and improve it.

What Deep Learning and Neuroscience has in common and how they align

Deep Learning and Neuroscience has a lot in common. A neuron network of the brain and the functionalities can be represented with a deep neural network model which is very close to how our brain process information.

It has been found that when we see something we don’t know exactly what we see. The process that both of these systems follows is to start recognizing small elements and attributes of the object. Then they start to combine those features together to create an overview of the final object and understand it.

For example, an Artificial Intelligence neuron network when it is trying image recognition first it will focus on minor details such as the edges of our face. Then another neuron will focus on our eyes size and position, another neuron will focus on the mouth, ears, hair, etc. When we combine all the elements this neuron network can conclude with an estimate that the image is a human face.

This is a very similar process that our brain is using when we are looking into things. We recognise what we see based on specific attributes. And this also implies the error factor which exists in both cases. We watch something, we predict the outcome, we are making a mistake, re-evaluate the process and predict again.

This is a process that is being followed and this proves how useful it can be to analyze complicated data that has a lot of parameters more quickly. This research will and has already helped scientists to move forward and get a better understanding of the human brain.

Author: Kostas Fotos - Project Manager at Youpal

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